Microorganism identification method

ABSTRACT

A microorganism identification method according to the present invention includes a step of subjecting a sample containing microorganisms to mass spectrometry to obtain a mass spectrum, a step of reading a mass-to-charge ratio m/z of a peak derived from a marker protein from the mass spectrum, and an identification step of identifying which bacteria of serovar of Salmonella genus bacteria the microorganisms contained in the sample contain, based on the mass-to-charge ratio m/z, in which at least one of two types of ribosomal proteins S8 and Peptidylpropyl isomerase is used as the marker protein.

TECHNICAL FIELD

The present invention relates to a microorganism identification methodusing mass spectrometry.

BACKGROUND ART

Salmonella belongs to the family of enterobacteriaceae of gram-negativefacultative anaerobic bacilli, and three species of Salmonella enterica,Salmonella bongori and Salmonella subterranea belong to the genusSalmonella. Further, Salmonella enterica is classified into sixsubspecies (Salmonella (sometimes abbreviated as “S.”) enterica subsp.enterica, S. enterica subsp. salamae, S. enterica subsp. arizonae, S.enterica subsp. diarizonae, S. enterica subsp. houtenae, S. entericasubsp. indica).

There are about 2,500 serovars in the genus Salmonella, which aredecided by the Kauffmann-White classification based on the difference incombination of a cell wall lipopolysaccharide O antigen, and a flagellarprotein H antigen. Pathogenic Salmonella such as Salmonella causing foodpoisoning belongs mostly to S. enterica subsp. enterica. This subspeciesis also classified into about 1,500 types of serovars (Non PatentLiterature 1). Currently, in order to decide the serovar, anagglutination test with antisera is used. It is an O type test by slideagglutination and an H type test by test tube agglutination, and the Htype test increases mobility and performs phase induction for firstphase and second phase decision, thus requires time and proficientskills for serovar decision.

Some serovars have determined pathogenic hosts. For example, Typhi,Choleraesuis, Dublin and Gallinarum cause systemic infectionspecifically in humans, pigs, cattle, and chickens. However, many otherserovars infect multiple hosts like humans, domestic animals, pets andwild animals and become pathogens of nontyphoidal acute gastroenteritis(food poisoning). Infection routes of nontyphoidal Salmonella rangewidely such as environments such as rivers, wild animals, pets, andfoods (including secondary pollution as well as primary pollution suchas through rodents and insects). Serovar decision is important forinfection prevention and epidemiological analysis and has been used formore than 80 years (Non Patent Literature 2).

Highly detected serovars of nontyphoidal Salmonella infections in recentyears are Enteritidis, Thompson, Infantis, Typhimurium, Saintpaul,Braenderup, Schwarzengrund, Litchfield, and Montevideo (IASR HP(Reference Document 1)). In the Act on Domestic Animal InfectiousDiseases Control in Japan, when livestock is infected with Dublin,Enteritidis, Typhimurium or Choleraesuis, notification to the Ministryof Agriculture, Forestry and Fisheries is mandatory.

As methods for detecting Salmonella and deciding serovars, multiplex PCR(Non Patent Literatures 3 and 4), pulsed field gel electrophoresis (NonPatent Literature 5), multilocus sequence typing method (Non PatentLiterature 6) and the like have been reported so far. However, withmultiplex PCR, there are problems that only a few serovars are decided,or only a part of the O antigen and H antigen is decided, and the othermethods require a complicated operation and take time.

On the other hand, in recent years, the microorganism identificationtechnique by matrix-assisted laser desorption/ionization time-of-flightmass-spectrometry (MALDI-TOF MS) has spread rapidly in clinical and foodfields. This method is a method of identifying microorganisms based on amass spectral pattern obtained using a very small amount ofmicroorganism sample, which can obtain an analysis result in a shorttime and also easily perform continuous analysis of multiple specimens.Therefore, easy and rapid microorganism identification is possible. Sofar, attempts have been made to identify Salmonella using MALDI-TOF MSby multiple research groups (Non Patent Literatures 7, 8, 9, 10)

Non Patent Literature 10 distinguishes subspecies of Salmonella entericasubsp. enterica and five major serovars by selecting a biomarker andpreparing a decision tree. While the research by Dieckmann et al.scrutinizes protein peaks very minutely, there are strains in whichbiomarker peak is present or absent, and it takes time to confirm thepeak,

CITATION LIST Patent Literature

Patent Literature 1: JP 2006-191922 A

Patent Literature 2: JP 2013-085517 A

Non Patent Literature

Non Patent Literature 1: ANTIGENIC FORMULAE OF THE SALMONELLA SEROVARS2007 9th edition WHO Collaborating Center for Reference and Research onSalmonella Patrick A. D. Grimont, François-Xavier Weill InstitutPasteur, 28 rue du Dr. Roux, 75724 Paris Cedex 15, France

Non Patent Literature 2: Winfield&Groisman, 2003, Fukuoka Institute ofHealth and Environmental Sciences

Non Patent Literature 3: M Akiba Et.al., Microbiological Methods, 2011,85, 9-15

Non Patent Literature 4: Y Hong et al., BMC microbiology 2008, 8: 178

Non Patent Literature 5: F Tenover, et al. Journal of clinicalmicrobiology 33.9 (1995): 2233.

Non Patent Literature 6: M Achtman, et al. PLoS Pathog 8.6 (2012):e1002776.

Non Patent Literature 7: Seng, Piseth, et al. Future microbiology 5.11(2010): 1733-1754.

Non Patent Literature 8: M Kuhns et al. PLoS One 7.6 (2012): e40004.

Non Patent Literature 9: R Dieckmann et al. AEM, 74.24 (2008):7767-7778.

Non Patent Literature 10: R Dieckmann, et al. (2011): AEM-02418.

Non Patent Literature 11: T. Ojima-Kato, et al. PLOS one 2014: e113458.

SUMMARY OF INVENTION Technical Problem

On the other hand, Patent Literature 1 shows that a method (S10-GERMSmethod) of attributing the type of protein to be the origin of the peakby associating the mass-to-charge ratio of the peak obtained by massspectrometry with a calculated mass estimated from the amino acidsequence obtained by translating the base sequence information of theribosomal protein gene, utilizing the fact that about half of the peaksobtained by subjecting microbial cells to mass spectrometry is derivedfrom ribosomal proteins, is useful (Patent Literature 1). According tothis method, it is possible to perform highly reliable microorganismidentification based on a theoretical basis using mass spectrometry andsoftware attached thereto (Patent Literature 2).

An object to be solved by the present invention is to provide a highlyreliable biomarker based on genetic information that can rapidly andeasily identify the serovar of Salmonella enterica subsp. enterica.

Solution to Problem

As a result of extensive studies, the present inventors have found thattwo types of ribosomal proteins S8 and Peptidylpropyl isomerase areuseful as marker proteins used for identifying which species of serovarof Salmonella genus bacteria is contained in a sample by massspectrometry, and it is possible to identify the serovar of Salmonellagenus bacteria reproducibly and quickly by using at least one of theseribosomal proteins, and have reached the present invention.

More specifically, a microorganism identification method according tothe present invention, which has been made to solve the above problems,includes

-   a) a step of subjecting a sample containing microorganisms to mass    spectrometry to obtain a mass spectrum,-   b) a step of reading a mass-to-charge ratio m/z of a peak derived    from a marker protein from the mass spectrum, and-   c) an identification step of identifying which bacteria of serovar    of Salmonella genus bacteria the microorganisms contained in the    sample contain, based on the mass-to-charge ratio m/z, in which at    least one of two types of ribosomal proteins S8 and Peptidylpropyl    isomerase is used as the marker protein.

In the above microorganism identification method, it is preferable thatthe serovars of Salmonella genus bacteria are classified using clusteranalysis using as an index the mass-to-charge ratio m/z derived from atleast 12 types of ribosomal proteins S8, L15, L17, L21, L25, S7, SODa,Peptidylpropyl isomerase, gns, YibT, YaiA and YciF as the markerprotein.

In this case, it is preferable to further include a step of generating adendrogram representing an identification result by the clusteranalysis.

In addition, in the above microorganism identification method, when theserovar of Salmonella genus bacteria is Orion, at least Peptidylpropylisomerase is preferably contained as the marker protein.

Moreover, when the serovar of Salmonella genus bacteria is Rissen, atleast S8 is preferably contained as the marker protein.

Also, when the serovar of Salmonella genus bacteria is Saintpaul, atleast L21, S7, YaiA and YciF are preferably contained as the markerprotein.

Further, when the serovar of Salmonella genus bacteria is Braenderup, atleast the group consisting of SOD, or gns and L25 is preferablycontained as the marker protein.

Furthermore, when the serovar of Salmonella genus bacteria is Montevideoor Schwarzengrund, at least one of SOD and L21, and S7 are preferablycontained as the marker protein.

Also, when the serovar of Salmonella genus bacteria is Enteritidis, atleast SOD, L17 and S7 are preferably contained as the marker protein.

Further, when the serovar of Salmonella genus bacteria is Infantis, atleast SOD, L21, S7, YibT and YciF are preferably contained as the markerprotein.

Advantageous Effects of Invention

According to the present invention, since a ribosomal protein showing amutation peculiar to the serovar of Salmonella genus bacteria is used asthe marker protein, the serovar of Salmonella genus bacteria can bereproducibly and quickly identified. Also, by using a ribosomal proteinshowing a mutation peculiar to the serovar of Salmonella genus bacteriaas the marker protein and performing a cluster analysis using themass-to-charge ratio m/z of the peak derived from the marker protein onthe mass spectrum as an index, the serovars of Salmonella genus bacteriacontained in a plurality of samples can be collectively identified.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a configuration diagram showing a main part of a microorganismidentification system used for a microorganism identification methodaccording to the present invention.

FIG. 2 is a flowchart showing an example of a procedure of amicroorganism identification method according to the present invention.

FIG. 3 shows a list of species name, subspecies name and serovar ofSalmonella genus bacteria used in examples.

FIG. 4 shows relationships between a combination of an agglutinatedimmune serum and a serovar.

FIG. 5 shows a list of primers used in examples.

FIG. 6 shows a mass of each amino acid.

FIG. 7A shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 1).

FIG. 7B shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 2).

FIG. 7C shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 3).

FIG. 7D shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 4).

FIG. 7E shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 5).

FIG. 7F shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 6).

FIG. 7G shows a list of theoretical mass values of each ribosomalprotein of Salmonella genus bacteria used in examples and measuredvalues by MALDI-TOF MS (part 7).

FIG. 8A is attribution results based on measured values of 12 types ofribosomal proteins (part 1).

FIG. 8B is attribution results based on measured values of 12 types ofribosomal proteins (part 2).

FIG. 8C is attribution results based on measured values of 12 types ofribosomal proteins (part 3).

FIG. 8D is attribution results based on measured values of 12 types ofribosomal proteins (part 4).

FIG. 9 is a chart obtained by MALDI-TOP MS measurement.

FIG. 10A is identification results by SARAMIS (part 1).

FIG. 10B is identification results by SARAMIS (part 2).

FIG. 11 is a peak chart of ribosomal protein SOD.

FIG. 12 is a peak chart of ribosomal protein L17.

FIG. 13 is a peak chart of ribosomal protein L21.

FIG. 14 is a peak chart of ribosomal protein S8.

FIG. 15 is a peak chart of ribosomal protein L15.

FIG. 16 is a peak chart of ribosomal protein S7.

FIG. 17 is a peak chart of ribosomal protein gns.

FIG. 18 is a peak chart of ribosomal protein YibT.

FIG. 19 is a peak chart of ribosomal protein ppic.

FIG. 20 is a peak chart of ribosomal protein L25.

FIG. 21 is a peak chart of ribosomal protein YaiA.

FIG. 22 is a peak chart of ribosomal protein YciF.

FIG. 23 is a dendrogram generated using 12 types of ribosomal proteins.

FIG. 24A is DNA sequences of ribosomal protein S8 (part 1).

FIG. 24B is DNA sequences of ribosomal protein S8 (part 2).

FIG. 24C is DNA sequences of ribosomal protein S8 (part 3).

FIG. 24D is DNA sequences of ribosomal protein S8 (part 4).

FIG. 25A is DNA sequences of ribosomal protein L15 (part 1).

FIG. 25B is DNA sequences of ribosomal protein L15 (part 2).

FIG. 25C is DNA sequences of ribosomal protein L15 (part 3).

FIG. 25D is DNA sequences of ribosomal protein L15 (part 4).

FIG. 25E is DNA sequences of ribosomal protein L15 (part 5).

FIG. 26A is DNA sequences of ribosomal protein L17 (part 1).

FIG. 26B is DNA sequences of ribosomal protein L17 (part 2).

FIG. 26C is DNA sequences of ribosomal protein L17 (part 3).

FIG. 26D is DNA sequences of ribosomal protein L17 (part 4).

FIG. 26E is DNA sequences of ribosomal protein L17 (part 5).

FIG. 27A is DNA sequences of ribosomal protein sodA (part 1).

FIG. 27B is DNA sequences of ribosomal protein sodA (part 2).

FIG. 27C is DNA sequences of ribosomal protein sodA (part 3).

FIG. 27D is DNA sequences of ribosomal protein sodA (part 4).

FIG. 27E is DNA sequences of ribosomal protein sodA (part 5).

FIG. 27F is DNA sequences of ribosomal protein sodA (part 6).

FIG. 27G is DNA sequences of ribosomal protein sodA (part 7).

FIG. 28A is DNA sequences of ribosomal protein L21 (part 1).

FIG. 28B is DNA sequences of ribosomal protein L21 (part 2).

FIG. 28C is DNA sequences of ribosomal protein L21 (part 3).

FIG. 28D is DNA sequences of ribosomal protein L21 (part 4).

FIG. 29A is DNA sequences of ribosomal protein L25 (part 1).

FIG. 29B is DNA sequences of ribosomal protein L25 (part 2).

FIG. 29C is DNA sequences of ribosomal protein L25 (part 3).

FIG. 30A is DNA sequences of ribosomal protein S7 (part 1).

FIG. 30B is DNA sequences of ribosomal protein S7 (part 2).

FIG. 30C is DNA sequences of ribosomal protein S7 (part 3).

FIG. 30D is DNA sequences of ribosomal protein S7 (part 4).

FIG. 30E is DNA sequences of ribosomal protein S7 (part 5).

FIG. 31A is DNA sequences of ribosomal protein gns (part 1).

FIG. 31B is DNA sequences of ribosomal protein gns (part 2).

FIG. 32A is DNA sequences of ribosomal protein yibT (part 1).

FIG. 32B is DNA sequences of ribosomal protein yibT (part 2).

FIG. 33A is DNA sequences of ribosomal protein ppiC (part 1).

FIG. 33B is DNA sequences of ribosomal protein ppiC (part 2).

FIG. 34 is DNA sequences of ribosomal protein yaiA.

FIG. 35A is DNA sequences of ribosomal protein yciF (part 1).

FIG. 35B is DNA sequences of ribosomal protein yciF (part 2).

FIG. 36A is amino acid sequences of ribosomal protein SOD (part 1).

FIG. 36B is amino acid sequences of ribosomal protein SOD (part 2).

FIG. 36C is amino acid sequences of ribosomal protein SOD (part 3).

FIG. 37A is amino acid sequences of ribosomal protein L17 (part 1).

FIG. 37B is amino acid sequences of ribosomal protein L17 (part 2).

FIG. 38A is amino acid sequences of ribosomal protein L21 (part 1).

FIG. 38B is amino acid sequences of ribosomal protein L21 (part 2).

FIG. 39A is amino acid sequences of ribosomal protein S8 (part 1).

FIG. 39B is amino acid sequences of ribosomal protein S8 (part 2).

FIG. 40A is amino acid sequences of ribosomal protein L15 (part 1).

FIG. 40B is amino acid sequences of ribosomal protein L15 (part 2).

FIG. 41A is amino acid sequences of ribosomal protein S7 (part 1).

FIG. 41B is amino acid sequences of ribosomal protein S7 (part 2).

FIG. 42 is amino acid sequences of ribosomal protein gns.

FIG. 43 is amino acid sequences of the ribosomal protein YibT.

FIG. 44 is amino acid sequences of the ribosomal protein ppic.

FIG. 45 is amino acid sequences of ribosomal protein L25.

FIG. 46 is amino acid sequences of ribosomal protein YaiA.

FIG. 47 is amino acid sequences of ribosomal protein YciF.

DESCRIPTION OF EMBODIMENTS

Hereinafter, a specific embodiment of the microorganism identificationmethod according to the present invention will be described.

FIG. 1 is an overview of a microorganism identification system used fora microorganism identification method according to the presentinvention. This microorganism identification system is roughly composedof a mass spectrometry unit 10 and a microorganism discrimination unit20. The mass spectrometry unit 10 includes an ionization section 11 forionizing molecules and atoms in a sample by a matrix-assisted laserdesorption ionization (MALDI) method, a time-of-flight mass separator(TOF) 12 for separating various kinds of ions emitted from theionization section 11 according to the mass-to-charge ratio.

The TOF 12 includes an extraction electrode 13 for extracting ions fromthe ionization section 11 and leading the ions to an ion flight space inthe TOF 12, and a detector 14 for detecting ions mass-separated in theion flight space.

The substance of the microorganism discrimination unit 20 is a computersuch as a workstation or a personal computer, in which a CentralProcessing Unit (CPU) 21 that is a central processing unit, a memory 22,a display section 23 consisting of a Liquid Crystal Display (LCD) andthe like, an input section 24 consisting of a keyboard, a mouse and thelike, and a storage section 30 consisting of a mass storage device suchas a hard disk and a SSD (Solid State Drive) are connected to eachother. In the storage section 30, an Operating System (OS) 31, aspectrum generation program 32, a genus/species decision program 33, anda subclass decision program 35 (program according to the presentinvention) are stored, and also a first database 34 and a seconddatabase 36 are housed. The microorganism discrimination unit 20 furtherincludes an interface (I/F) 25 for direct connection with an externaldevice and for controlling connection with an external device or thelike via a network such as a LAN (Local Area Network), and is connectedto the mass spectrometry unit 10 via a network cable NW (or wirelessLAN) from the interface 25.

In FIG. 1, the spectrum acquisition part 37, the m/z reading part 38,the subclass determination part 39, the cluster analysis part 40, andthe dendrogram (system diagram) generation part 41 are shown as relatedwith the subclass decision program 35. Basically, these are allfunctional means realized by software by the CPU 21 executing thesubclass decision program 35. The subclass decision program 35 is notnecessarily a single program but may be a function incorporated in apart of a program for controlling the genus/species decision program 33or the mass spectrometry unit 10, for example, and its form is notparticularly limited. As the genus/species decision program 33, forexample, a program for performing microorganism identification by aconventional fingerprint method or the like can be used.

Also, in FIG. 1, a configuration in which the spectrum generationprogram 32, the genus/species decision program 33, and the subclassdecision program 35, the first database 34, and the second database 36are mounted on the terminal operated by the user is shown. However, aconfiguration in which at least part or all of them is provided inanother device connected to the terminal via the computer network, andprocessing according to a program provided in the another device and/oraccess to the database is executed according to an instruction from theterminal may be used.

A large number of mass lists related to known microorganisms areregistered in the first database 34 of the storage section 30. This masslist lists the mass-to-charge ratios of ions detected upon massspectrometry of certain microbial cells. In addition to the informationof the mass-to-charge ratio, at least, information (classificationinformation) of the classification group (family, genus, species, etc.)to which the microbial cells belong is contained. Such mass list isdesirably created on the basis of data (measured data) obtained byactually subjecting various microbial cells to mass spectrometry inadvance by the same ionization method and mass separation method asthose by the mass spectrometry unit 10.

When creating a mass list from the measured data, first, a peakappearing in a predetermined mass-to-charge ratio range is extractedfrom the mass spectrum acquired as the measured data. At this time, bysetting the mass-to-charge ratio range to about 2,000 to 35,000, it ispossible to mainly extract a protein-derived peak. Also, by extractingonly peaks whose height (relative intensity) is equal to or greater thana predetermined threshold, undesirable peaks (noise) can be excluded.Since the ribosomal protein group is expressed in a large amount in thecell, most of the mass-to-charge ratio described in the mass list can bederived from the ribosomal protein by appropriately setting thethreshold. Then, the mass-to-charge ratios (m/z) of the peaks extractedas above are listed for each cell and registered in the first database34 after adding the classification information and the like. In order tosuppress variations in gene expression due to culture conditions, it isdesirable to standardize culture conditions in advance for eachmicrobial cell used for collecting the measured data.

In the second database 36 of the storage section 30, information onmarker proteins for identifying known microorganisms by a classification(subspecies, pathotype, serovar, strain, etc.) lower than the species isregistered. Information on the marker protein includes at leastinformation on the mass-to-charge ratio (m/z) of the marker protein inthe known microorganisms. In the second database 36 in the presentembodiment, the values of mass-to-charge ratio m/z derived from at least12 types of ribosomal proteins S8, L15, L17, L21, L25, S7, SODa,Peptidylpropyl isomerase, gns, YibT, YaiA and. YciF are stored, asinformation on a marker protein for determining which serovar ofSalmonella genus bacteria a test microorganism is. The values ofmass-to-charge ratio of these ribosomal proteins will be describedlater.

It is desirable that the values of mass-to-charge ratio of the markerprotein stored in the second database 36 are selected by comparing thecalculated mass obtained by translating the base sequence of each markerprotein into an amino acid sequence with the mass-to-charge ratiodetected by actual measurement. The base sequence of the marker proteincan be decided by sequence, or also can use a public database, forexample, one acquired from a database of NCBI (National Center forBiotechnology Information) or the like. When obtaining the calculatedmass from the above amino acid sequence, it is desirable to considercleavage of the N-terminal methionine residue as a post-translationalmodification. Specifically, when the penultimate amino acid residue isGly, Ala, Ser, Pro, Val, Thr or Cys, the theoretical value is calculatedassuming that the N-terminal methionine is cleaved. In addition, sincemolecules added with protons are actually observed by MALDI-TOF MS, itis desirable to obtain the calculated mass also considering the protons(that is, the theoretical value of mass-to-charge ratio of ions obtainedwhen each protein is analyzed by MALDI-TOF MS).

The procedure for identifying the serovar of Salmonella genus bacteriausing the microorganism identification system according to thisembodiment will be described with reference to a flowchart.

First, the user prepares a sample containing constituents of testmicroorganism, sets the sample in the mass spectrometry unit 10, andperforms mass spectrometry. At this time, as the sample, in addition toa cell extract, or a cellular constituent such as a ribosomal proteinpurified from a cell extract, a bacterial cell or a cell suspension canbe also used as it is.

The spectrum generation program 32 acquires a detection signal acquiredfrom the detector 14 of the mass spectrometry unit 10 via the interface25, and generates a mass spectrum of the test microorganism based on thedetection signal (Step S101).

Next, the species decision program 33 collates the mass spectrum of thetest microorganism with the mass lists of the known microorganismsrecorded in the first database 34, and extracts a mass list of the testmicroorganism having a mass-to-charge ratio pattern similar to the massspectrum of the test microorganism, for example, a mass list containingmany peaks that coincide with each peak in the mass spectrum of the testmicroorganism in a predetermined error range (Step S102). The speciesdecision program 33 subsequently refers to the classificationinformation stored in the first database 34 in association with the masslist extracted in Step S102 to specify a species to which the knownmicroorganism corresponding to the mass list belongs (Step S103). Then,when this species is not Salmonella genus bacteria (No in Step S104),the species is outputted to the display section 23 as a species of thetest microorganism (Step S116), and the identification processing isterminated. On the other hand, when the species is Salmonella genusbacteria (Yes in Step S104), then the process proceeds to theidentification processing by the subclass decision program 35. When itis determined in advance that the sample contains Salmonella genusbacteria by other methods, the process may proceeds to the subclassdecision program 35 without utilizing the species decision program usingthe mass spectrum.

In the subclass decision program 35, first, the subclass determinationpart 39 reads out each of the values of mass-to-charge ratio of 12 typesof ribosomal proteins S8, L15, L17, L21, L25, S7, SODa, Peptidylpropylisomerase, gns, YibT, YaiA and YciF from the second database 36 (StepS105). Subsequently, the spectrum acquisition part 37 acquires the massspectrum of the test microorganism generated in Step S101. Then, the m/zreading part 38 selects peaks appearing in the mass-to-charge ratiorange stored in the second database 36 in association with each markerprotein on the mass spectrum as peaks corresponding to each markerprotein, and reads the mass-to-charge ratio (Step S106). And, clusteranalysis using the read mass-to-charge ratio as an index is performed.Specifically, the subclass determination part 39 compares themass-to-charge ratio with the values of mass-to-charge ratio of eachmarker protein read out from the second database 36 and decidesattribution of the protein with respect to the read mass-to-charge ratio(Step S107). Then, cluster analysis is performed based on the decidedattribution to determine the serovar of the test microorganism (StepS108), and the result is output to the display section 23 as theidentification result of the test microorganism (Step S109).

Although the embodiments for carrying out the present invention havebeen described above with reference to the drawings, the presentinvention is not limited to the above-described embodiments, andappropriate modifications are permitted within the scope of the gist ofthe present invention.

EXAMPLES (1) Strains Used

As described in FIG. 3, a total of 64 strains of Salmonella availablefrom the National Institute of Technology and Evaluation Nite BiologicalResource Center (NBRC), Microbe Division/Japan Collection ofMicroorganisms (JCM) RIKEN BioResource Research Center (Tsukuba),National Bioresource Project GTC Collection (Gifu) and the American TypeCulture Collection (Manassas, Va., USA) that are strain culturecollection, isolates from Japan Food Research Laboratories and isolatesfrom Hyogo Prefectural Institute of Public Health science were used foranalysis. The serovar of Salmonella enterica subsp. enterica was decidedby multiplex PCR method reported by Salmonella immune serum “Seiken”(DENKA SEIKEN Co., Ltd.) and Non Patent Literatures 3 and 4. The strainswere classified into 22 serovars by this method. FIG. 4 showsrelationships between O-antigen immune serum and a serovar.

(2) Analysis of DNA

Among the primers used in Escherichia coli database creation (Non PatentLiterature 11), those which cannot be shared with Salmonella genusbacteria were designed based on consensus sequences. The designedprimers are shown in FIG. 5. Using these primers, DNA sequences ofS10-spc-alpha operon and protein genes that could be biomarkers wereanalyzed. Specifically, genomic extraction was performed from eachstrain by a conventional method, and PCR was carried out using KOD plusas a template to amplify a target gene region. The obtained PCR productwas purified and used as a template for sequence analysis. Sequenceanalysis was performed using Big Dye ver. 3.1 Cycle Sequencing Kit(Applied Biosystems, Foster City, Calif., USA). The DNA sequence of thegene was converted to the amino acid sequence of each gene, and themass-to-charge ratio was calculated based on the amino acid mass in FIG.6 to obtain a theoretical mass value.

(3) Analysis by MALDI-TOF MS

Bacterial cells grown in Luria Agar medium (Sigma-Aldrich Japan, Tokyo,Japan) were recovered and approximately 2 colonies of bacterial cellswere added in 10 μL of a sinapinic matrix agent (25 mg/mL sinapinic acid(Wako Pure Chemical Industries, Ltd., Osaka, Japan) in 50 v/v %acetonitrile and 0.6 v/v % trifluoroacetic acid solution) and stirredwell, and 1.2 μL out of the solution was loaded on a sample plate andair-dried. For MALDI-TOF MS measurement, the sample was measured inpositive linear mode, at spectral range of 2000 m/z to 35000 m/z usingAXIMA microorganism identification system (Shimadzu Corporation, KyotoCity, Japan). The above-described calculated mass was matched with themeasured mass-to-charge ratio with a tolerance of 500 ppm, and propermodification was made. The calibration of the mass spectrometer wasperformed according to the instruction manual, using Escherichia coliDH5α strain.

(4) Construction of Salmonella enterica subsp. enterica Database

By comparing the theoretical mass values of the ribosomal proteinsobtained in the above (2) with the peak chart by MALDI-TOF MS obtainedin (3), it was confirmed that there was no difference between thetheoretical values obtained from gene sequences and the measured values,regarding the protein which could be detected by actual measurement. Thetheoretical and measured values of the ribosomal proteins in theS10-spc-α operon and proteins that can be other biomarkers showingdifferent masses depending on the strain are summarized as a database asshown in FIGS. 7A to 7G.

The numbers shown in FIGS. 7A to 7G are the theoretical mass of themass-to-charge ratio (m/z) obtained from genes. In addition, symbols“◯”, “Δ”, and “x” represent mass peak detection results in actualmeasurement. Specifically, the symbol “◯” indicates that it was detectedas a peak within the 500 ppm range of the theoretical value at thedefault peak processing setting (threshold offset; 0.015 mV, thresholdresponse; 1.200) of AXIMA microorganism identification system, and thesymbol “x” indicates that there was a case where a peak could not bedetected. In addition, the symbol “Δ” means that the theoretical massdifference in each strain or the difference from other protein peaks was500 ppm or less, respectively, and the mass difference could not beidentified even when a peak was detected.

As can be seen from FIGS. 7A to 7G, it was showed that the theoreticalmass values of the ribosomal proteins L23, L16, L24, S8, L6, S5, L15 andL17 encoded in the s10-spc-alpha operon and L21, L25, S7, SODa, gns,YibT, Peptidylpropyl isomerase, YaiA and YciF outside the operon (total17 types) differ depending on the strain of Salmonella enterica subsp.enterica, thus are possibly useful protein markers that can be used forserovar identification of Salmonella enterica subsp. enterica.

However, while it can be seen that L23, L16, L24, L6 and S5 have strainswhose theoretical mass differences are separated by 500 ppm or more andcan be a powerful biomarker for identification of these strains, therewas a strain that could not be detected in actual measurement.

On the other hand, a total of seven types of proteins, S8, L15, L17,L21, L25, S7 and Peptidylpropyl isomerase, were stably detectedirrespective of the strains, and the mass difference by the strains wasalso 500 ppm or more. Therefore, these proteins were found useful asbiomarkers for serovar identification of Salmonella enterica subsp.enterica in MALDI-TOF MS.

SODa is an important biomarker for serovar identification of Salmonellaenterica subsp. enterica, but the genotypes were varied and sevendifferent mass-to-charge ratios were confirmed. All of thesemass-to-charge ratios are as large as m/z around 23000, and in thisregion, the analysis accuracy of currently provided MALDI-TOF MS is lowunless the difference between the other mass-to-charge ratios is 800 ppmor more, thus SODa cannot identify the serovars. Therefore, four typesthat can identify the serovar at this time were used as biomarkers.Regarding gns, YibT, YaiA and YciF, contamination peaks exist in one ofthe theoretical mass values, but since serovars Infantis, Thompson andTyphimuriunm are proteins that are mutated specifically, only thetheoretical mass value without contamination peak was used as abiomarker. Therefore, 12 types of proteins were used as biomarkers forSalmonella enterica subsp. enterica serovar identification.

(5) Attribution of Measured Values of MALDI-TOFMS by Software

Based on the above, using a total of 12 types of proteins, 8 types ofproteins S8, L15, L17, L21, L25, S7, SODa and Peptidylpropyl isomerasethat are stably detected regardless of the strain and 4 types ofproteins gns, YibT, YaiA and YciF, as biomarkers, their theoretical massvalues were registered in the software as shown in Patent Literature 2.

5: 22962.8 that was within the mass difference of 800 ppm of SODa wasregistered as the closest 1: 22948.82, and 6: 22996.82 and 7: 23004.88as 2: 23010.84. In addition, gns, YibT, YaiA and YciF in whichcontamination peaks exist are registered as 6483.51, 8023.08, 7110.89and 18643.13/18653.16, respectively.

Next, measured data in MALDI-TOF MS was analyzed with this software, andwhether each biomarker was correctly attributed as a registered masspeak was examined. As a result, as shown in FIGS. 8A to 8G, allbiomarker mass peaks of all the strains were attributed as registeredmass numbers. Each attribution mass pattern was classified into groups 1to 31, and compared with the serovar of each strain. Then, it was foundthat Typhimurium belongs to 1, 2 and 3, O4 group with unknown serovar to4 and 5, Saintpaul to 6, O18 group with unknown serovar to 7. Orion to8, Braenderup to 9, Montevideo and Schwarzengrund to 10, Schwarzengrundto 11, Abony and Pakistan to 12, Enteritidis to 13 and 14, Rissen to 15,Gallinarum Pullorum to 16, Altona to 17, Amsterdam to 18, Infantis to 19and 20, Istanbul to 21, O4 group with unknown serovar to 22, Manhattanto 23, Mbandaka to 24, Senftenberg and O1, 3 and 19 groups with unknownserovar to 25, Thompson to 26, O4 group with unknown serovar to 27, O7group with unknown serovar to 28, Brandenburg, Minnesota and Saintpaulto 29, Brandenburg and Saintpaul to 30, and Choleraesuis strain to 31.

Based on the above, it was found that use of the mass of S8 (m/z13996.36 or 14008.41), L15 (m/z 14967.38, 14981.41 or 14948.33), L17(m/z 14395.61 or 14381.59), L21 (m/z 11579.36 or 11565.33), L25 (m/z10542.19 or 10528.17), S7 (m/z 17460.15, 17474.18 or 17432.1), SODa (m/z22948.82, 23010.84, 22976.83 or 22918.79), Peptidylpropyl isomerase (m/z10198.07 or 10216.11), gns (m/z 6483.51), YibT (m/z 8023.08), YaiA (m/z7110.89) and YciF (m/z 18643.13) as biomarkers for MALDI-TOF MS analysisis useful for serovar identification of Salmonella enterica subsp.enterica.

Among the biomarkers found out this time, 10 types except S8 andPeptidylpropyl isomerase have been reported in Non Patent Literature 10.However, Non Patent Literature 10 requires confirmation of each peak oneby one, thus takes time for spectral analysis of MALDI-TOF MS foridentifying serovar. Also, as to the mass-to-charge ratio m/z 6036reported to be an important peak for identification of Enteriridis inNon Patent Literature 10, a peak was not confirmed in 5 strains out of32 strains in Non Patent Literature 10, and in this example, a peakcould not be confirmed in 8 strains out of 35 strains. Therefore, it wasnot used as a biomarker for serovar identification of Salmonellaenterica subsp. enterica.

By adding S8 and Peptidylpropyl isomerase to the biomarkers and using 12types of carefully selected proteins as biomarkers, it became possibleto provide a database that automatically identifies Salmonella enterivasubsp. enterica to 31 groups for the first time.

(6) Comparison with Fingerprint Method (SARAMIS)

In fact, the identification result by the existing fingerprint method(SARAMIS) was compared with the identification result using thebiomarker theoretical mass value shown in Table 6 as indices. First, inactual measurement in MALDI-TOF MS, a chart as shown in FIG. 9 wasobtained. This result was analyzed by SARAMIS according to theinstruction manual of AXIMA microorganism identification system. Resultsthus obtained are shown in FIG. 10A and FIG. 10B. As can be seen fromthese figures, all Salmonella genus bacteria used in the sample wereidentified as Salmonella enterica subsp. enterica in 91% to 99.9%, andspecies identification and serovar identification were not performed.

Therefore, whether measurement results of strains of differentsubspecies can be identified based on the theoretical mass databaseshown in FIG. 8A was attempted. FIGS. 11 to 22 are enlarged views of 12types of biomarker peak portions of the charts of FIG. 8. As can be seenfrom FIGS. 11 to 22, peaks can be distinguished since each biomarkermass is shifted. When compared with the measured values of 12 types ofbiomarkers and attributed, they agreed with the results shown in FIGS.8A to 8D.

Next, cluster analysis was performed using the attribution results of 12types of ribosomal proteins, and dendrogram was generated. The resultsare shown in FIG. 23. In this method, although serovars of Infantis,Brandenburg, Minnesota and Saintpaul could not be identified, otherserovars could be almost identified.

Based on the above, the following can be seen.

SODa, S7 and gns are involved in the identification of multiple serovarsand are particularly important as biomarkers for serovar identificationof Salmonella enterica subsp. enterica.

Moreover, Enteritidis, Mbandaka and Choleraesuis can be identified fromother serovars by combination of SODa and S7 mutation.

Furthermore, Infantis is identified, and Enteritidis and Mbandaka areidentified by gns.

Typhimurium, which is the top of serovar responsible for nontyphoidalSalmonella infections, is separated by YaiA, and Thompson by YibT. Also.Pullorm (Gallinarum) is identified by L17, Rissen by S8, Orion byPeptidylpropyl isomerase, and Altona by L15. L25 separates Infantis andAmsterdam, and L21 is important to identify Montevideo andShwarzengrund, Minnesota. YciF is important for identification ofInfantis.

(7) Gene Sequence and Amino Acid Sequence of Biomarkers

DNA sequences and amino acid sequences in each strain of a total of 12types of ribosomal proteins, S8, L15 and L17 encoded in theS10-spc-alpha operon and SODa, L21, L25, S7, gns, YibT, Peptidylpropylisomerase and YciF outside the operon, which exhibit theoretical massvalues different depending on the strain of Salmonella enterica subsp.enterica, are summarized in FIGS. 24 to 47.

REFERENCE SIGNS LIST

-   10 . . . Mass Spectrometry Unit-   11 . . . Ionization Section-   12 . . . TOF-   13 . . . Extraction Electrode-   14 . . . Detector-   20 . . . Microorganism Discrimination Unit-   21 . . . CPU-   22 . . . Memory-   23 . . . Display Section-   24 . . . Input Section-   25 . . . I/F-   30 . . . Storage Section-   31 . . . OS-   32 . . . Spectrum Generation Program-   33 . . . Genus/Species Decision Program-   34 . . . First Database-   35 . . . Subclass Decision Program-   36 . . . Second Database-   37 . . . Spectrum Acquisition Part-   38 . . . m/z Reading Part-   39 . . . Subclass Determination Part-   40 . . . Cluster Analysis Part-   41 . . . Dendrogram Generation Part

1. A microorganism identification method comprising a) a step ofsubjecting a sample containing microorganisms to mass spectrometry toobtain a mass spectrum, b) a step of reading a mass-to-charge ratio m/zof a peak derived from a marker protein from the mass spectrum, and c)an identification step of identifying which bacteria of serovar ofSalmonella genus bacteria the microorganisms contained in the samplecontain, based on the mass-to-charge ratio m/z, wherein at least one oftwo types of ribosomal proteins S8 and Peptidylpropyl isomerase is usedas the marker protein.
 2. The microorganism identification methodaccording to claim 1, wherein the serovars of Salmonella genus bacteriaare classified using cluster analysis using as an index themass-to-charge ratio m/z derived from at least 12 types of ribosomalproteins S8, L15, L17, L21, L25, S7, SODa, Peptidylpropyl isomerase,gns, YibT, YaiA and YciF as the marker protein.
 3. The microorganismidentification method according to claim 2, further comprising a step ofgenerating a dendrogram representing an identification result by thecluster analysis.
 4. The microorganism identification method accordingto claim 1, wherein the serovar of Salmonella genus bacteria is Orion,and at least Peptidylpropyl isomerase is contained as the markerprotein.
 5. The microorganism identification method according to claim1, wherein the serovar of Salmonella genus bacteria is Rissen, and atleast S8 is contained as the marker protein.
 6. A non-transitorycomputer-readable medium storing a program for causing a computer toexecute each step according to claim
 1. 7. A non-transitorycomputer-readable medium storing a program for causing a computer toexecute each step according to claim
 2. 8. A non-transitorycomputer-readable medium storing a program for causing a computer toexecute each step according to claim
 3. 9. A non-transitorycomputer-readable medium storing a program for causing a computer toexecute each step according to claim
 4. 10. A non-transitorycomputer-readable medium storing a program for causing a computer toexecute each step according to claim 5.